Assessing novel and not so novel cognitive strategies to improve episodic memory performance in schizophrenia
Bibliographic record
Abstract
Episodic memory (EM) impairment is well documented in the schizophrenia literature.Relational memory (RM), a component of episodic memory that refers to one's ability to make associations between items, events and context, is particularly impaired.Considering the relationship between memory performance and clinical and functional outcomes in schizophrenia, there is an important need to remediate those impairments.Interventions to improve cognitive capacity in schizophrenia AbstractWe investigated the feasibility of a short intervention using the Method of Loci (MoL), a wellknown visuospatial mnemonic, to improve episodic memory recall performance in schizophrenia.The MoL training protocol comprised encoding and recall of two lists of items (words and images), a training session and practice with MoL.Then, participants had the opportunity to put into practice the newly learned MoL and were instructed to encode and recall two new lists of items using.This approach was first validated with healthy individuals (N = 71).Subsequently, five individuals with schizophrenia completed the protocol.Improvement in healthy individuals was observed for the word list (Wilcoxon effect size r = 0.15).No significant memory improvement was denoted in the schizophrenia group, possibly due to participants' difficulties using the method efficiently and due to fatigue.The MoL seems to require episodic memory, working memory monitoring and executive functions, making it suboptimal for a population with impairments in all those domains.Future research should examine the use of other strategies, better suited for individuals with cognitive impairments like those found in schizophrenia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".